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A Simplified Workflow for the Prediction of Putative Viral Reads Using NIPT Data
Shabnam Shahidi1, Atousa Dabiri Oskoei2, Akbar Mohammadzadeh1
1Department of Medical Genetics and Molecular Medicine, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran.
Prenatal Diagnosis
|August 5, 2026
Summary
This study introduces an efficient computational workflow to detect viral DNA in pregnant women using non-invasive prenatal testing (NIPT) data. The method identified diverse viral species in over 24% of participants, suggesting a broad maternal virome.
Area of Science:
- Genomics
- Virology
- Bioinformatics
Background:
- Non-invasive prenatal testing (NIPT) analyzes cell-free fetal DNA (cffDNA) for fetal chromosomal abnormalities.
- Emerging research indicates NIPT data may predict viral sequences, but current methods are not cost-effective for routine clinical use.
Purpose of the Study:
- To develop a straightforward and cost-effective computational workflow for identifying viral signatures in pregnant women using NIPT data.
- To investigate the prevalence and diversity of viral DNA in a cohort of 888 Iranian pregnant participants.
Main Methods:
- Compared two bioinformatic workflows for viral read prediction: a traditional method and a novel direct mapping approach.
- The proposed workflow aimed to minimize computational complexity and processing time while maintaining reproducibility.
Main Results:
- The developed workflow demonstrated comparable reproducibility to the conventional method.
- Viral DNA was detected in 24.2% of samples, representing 29 distinct viral species.
- The findings suggest a diverse maternal virome detectable through NIPT data analysis.
Conclusions:
- A computationally efficient workflow for in silico prediction of viral-like sequences from NIPT data has been established.
- Experimental validation is crucial to confirm the presence, viability, and clinical significance of the detected viral sequences.
